Registration is a task in image processing involving matching of two or more images acquired, for example, at different times, from different sensors, or from different perspectives. Registration is typically used in many applications such as integration of information acquired from different sensors, detection of changes in images acquired at different times or under different conditions, assembly of three-dimensional information from images in conditions of movement of the camera or objects in the scene, model-based object recognition, and others. Registration of two images involves determination of a transformation so that individual points in one image can be mapped to a point in a second image.
Image registration may be defined as a mapping between two images. Typically, the mapping is spatial with respect to a parameter such as intensity. Brown, L. G., in “A survey of image registration techniques”, ACM Computing Surveys, vol. 24, no. 2, pp. 325-376 (1992), describes image restoration theory in terms of analysis of two 2-dimensional array images I1 and I2. In Brown's example, the images I1(x,y) and I2(x,y) each map to respective intensity values. The mapping between images can be expressed as:I2(x,y)=g(I1(ƒ(x,y)))where ƒ is a 2-dimensional spatial coordinate transformation, for example,(x′,y′)=ƒ(x,y)and g is a one-dimensional intensity or radiometric transformation.
A goal of registration is to determine optimal spatial and intensity transformations so that the images are matched with regard to a misregistration source. Generally, a key to an image registration solution is determination of spatial and geometric transformation. The spatial and geometric transformation can be expressed parametrically as two single-valued functions, ƒx,ƒy:I2(x,y)=I1(ƒx(x,y),(ƒy(x,y)),to enable a natural implementation. If the geometric transformation can be expressed as a pair of separable functions, for example such that two consecutive one-dimensional or scan-line operations can be used to compute the transformationƒ(x,y)=ƒ1(x)∘ƒ2(y)to promote efficiency. Generally, function ƒ2 is applied to each row, then function ƒ2 is applied to each column.
Image registration may be used in a wide variety of applications such as medical imaging, vehicular machine vision systems, imaging systems for manufacturing control, document system control using imaging, and many others. For example, medical imaging systems may include magnetic resonance imaging (MRI), x-ray computed tomography (CT), ultrasound imaging, nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT), and the like. Document handling systems often include scanners for conveying original documents to a scanning or input area.
Various image registration techniques have been developed. Many of the techniques address particular image registration problems. Existing image registration methods rarely involve a gauge of uncertainty associated with a solution and those that do generally rely on strict assumptions regarding the structure of data. Such assumptions are frequently violated in practical image registration applications.